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Software
  • MetaOmics: a suite of packages for microarray and genomic meta-analysis
    • MetaQC: quality control to determin inclusion/exclusion of studies in meta-analysis
    • MetaDE: detect DE (candidate marker) genes in meta-analysis
    • MetaPath: detect associated pathways in meta-analysis
    • MetaClust: gene clusering in meta-analysis
    • MetaPCA: dimension reductioin by PCA, sparse PCA and robust PCA in meta-analysis
    • MetaNetwork: meta-analysis to detect conserved and differential co-expression network modules
  • Inter-study prediction in mocroarray studies
  • Gene clustering methods in microarray or high-dimensional data analysis
  • QuantileMap : a visualization tool to compactlydemonstrate multiple (hundreds) distributions in genomic applications.
  • a set of R functions for cDNA array analysis:



  • Inter-study prediction in microarray studies:

    • Ratio-adjusted gene-wise normalization (rGN): Download

      Chunrong Cheng, Kui Shen, Chi Song, Jianhua Luo and George C Tseng. (2009) Ratio Adjustment and Calibration Scheme for Gene-wise Normalization to Enhance Microarray Inter-study Prediction. Bioinformatics. 25:1655-1661.

    • Module-based preidction approach (MBP):

      Zhibao Mi, Kui Shen, Nan Song, Chunrong Cheng, Chi Song and George C Tseng. (2010) Unsupervised module-based prediction approach for robust inter-study prediction in microarray data. Bioinformatics. 26: 2586-2593.


  • Gene clustering in microarray data: Our group has developed two complementary gene clustering methods for microarray data (or for clustering high-dimensional complex data in general). Both methods directly identify small and tight clusters in the data and allow a set of scattered genes without being clustered. Tight clustering utilizes resampling techniques to obtain consistent tight clusters in repeated subsampling evaluations. Penalized and weighted K-means extends the target function of K-means. It has faster computation than tight clustering and can allow incorporation of prior biological information.

    • Tight clustering: Download (ANSI C source code and R package), CRAN download

      George C. Tseng and Wing H. Wong. (2005) Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data. Biometrics.61:10-16.

    • PWKmeans: Download (C source code and PPAM package)

      George C. Tseng. (2007). Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data. Bioinformatics. 23:2247-2255.


  • Quantile maps: This is a visualization tool to compactly and unbiasedly demonstrate multiple (hundreds) distributions.

    George C. Tseng. (2009) Quantile map: Simultaneous visualization of patterns in many distributions with application to tandem mass spectrometry. Computational Statistics and Data Analysis. in press.


  • R functions for cDNA array analysis: a set of R functions for filtering, normalization, Bayesian hierarchical modelling and MCMC procedures in cDNA microarray analysis.

    The method is developed to assess gene expression level with replicates in cDNA microarray data. A Bayesian hierarchical model is established to model gene-specific replicate variations with prior information from calibration experiments. A version of empirical Bayes procedure is used. MCMC simulation is then used to generate the posterior distribution.

    This program provides a browser interface to implement methods described in the paper. The interface is written in JavaScript but runs in R at the background. The plug-in between JavaScript and R may no longer be maintained. In that case, users can still use the functions directly in R.


    George C. Tseng, Min-Kyu Oh, Lars Rohlin, James C. Liao, and Wing Hung Wong. (2001) Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Research. 29: 2549-2557.